1. 机器学习

  1. Hands-on Machine Learning with R

  2. The R in Spark: Learning Apache Spark with R

  3. 特征工程与特征选择 Feature Engineering and Selection: A Practical Approach for Predictive Models Max Kuhn and Kjell Johnson

  4. 分类与回归:caret 包 The caret Package

  5. 可解释的机器学习 Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

  6. 机器学习与R语言 Machine Learning in R

2. 统计图形

  1. 数据可视化基础 Fundamentals of Data Visualization

  2. 数据可视化与 R 语言 Data Visualisation with R — 100 Examples

  3. R 语言绘图手册 R Graphics Cookbook PDF

  4. 数据分析与图形艺术 ggplot2: Elegant Graphics for Data Analysis PDF 第三版正在写作中 https://github.com/hadley/ggplot2-book

  5. 数据可视化:实践指南 Data Visualization: A practical introduction Github

  6. 可视化数据分析与 R 语言 Graphical Data Analysis with R

3. 数据科学与 R 语言

4. 统计模型

5. 统计软件

  1. 道家的小无相功

  2. 开发 R 包

  3. 文本挖掘

  4. 动态文档

  5. 模型部署

更多在线书籍合集 https://bookdown.org ,要记住学习 R 语言的天花板不在编程而在统计。

6. 高等统计、高等概率论、机器学习、统计学习

  1. Handbook of mathematical functions

  2. The Boosttrap and Edgeworth Expansion

    • 蒙特卡洛、Boostrap、非参数分布
    • Michael I. Jordan 主页 PDF 下载
  3. Theory of Statistics PDF 下载

  4. Graphical models, exponential families, and variational inference.

    • Foundations and Trends in Machine Learning. Vol.1
    • 图模型 指数族 变分推断 M. J. Wainwright and M. I. Jordan PDF下载
  5. High-dimensional statistics: A non-asymptotic viewpoint

  6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

  7. An Introduction to Statistical Learning with Applications in R

  8. Statistical Learning with Sparsity: The Lasso and Generalizations

  9. Statistical Learning from a Regression Perspective

  10. Computer Age Statistical Inference: Algorithms, Evidence and Data Science

  11. Convergence of Stochastic Processes

  12. Foundations of Machine Learning

  13. Foundations of Data Science

  14. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville

  15. Pattern Recognition and Machine Learning

  16. Bayesian Reasoning and Machine Learning by David Barber

  17. Machine Learning: a Probabilistic Perspective

  18. All of Statistics A Concise Course in Statistical Inference

  19. Gaussian Processes for Machine Learning

  20. Information Theory, Inference, and Learning Algorithms

  21. Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

  22. Convex Optimization

  23. Tensorflow 内核和实现机制 https://github.com/horance-liu/tensorflow-internals

  24. 神经网络与深度学习 Neural Networks and Deep Learning

  25. 神经网络与深度学习 Neural Network and Deep Learning 邱锡鹏

  26. 迁移学习简明手册 https://github.com/jindongwang/transferlearning-tutorial

  27. 加强学习导论 中文网页版

  28. 动手学深度学习 Dive into Deep Learning https://zh.d2l.ai/ PDF

  29. 《机器学习》(西瓜书)公式推导解析 https://github.com/datawhalechina/pumpkin-book

  30. R 语言实现李航《统计学习方法》中的所有算法 https://bookdown.org/lyuchengrui/statisticallearningmethods/

8. 学习 Python

9. 词汇对照表 1

Machine Learning Statistics
network,graphs model
weights parameters
generalization test set performance
supervised learning regression/classification
unsupervised learning density estimation,clustering
large grant = $1,000,000 large grant = $50,000